Stein Variational Model Predictive Control

• URL: http://arxiv.org/abs/2011.07641v4
• Date: Mon, 12 Apr 2021 16:20:07 GMT
• Title: Stein Variational Model Predictive Control
• Authors: Alexander Lambert, Adam Fishman, Dieter Fox, Byron Boots, Fabio Ramos
• Abstract summary: Decision making under uncertainty is critical to real-world, autonomous systems. Model Predictive Control (MPC) methods have demonstrated favorable performance in practice, but remain limited when dealing with complex distributions. We show that this framework leads to successful planning in challenging, non optimal control problems.
• Score: 130.60527864489168
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Decision making under uncertainty is critical to real-world, autonomous systems. Model Predictive Control (MPC) methods have demonstrated favorable performance in practice, but remain limited when dealing with complex probability distributions. In this paper, we propose a generalization of MPC that represents a multitude of solutions as posterior distributions. By casting MPC as a Bayesian inference problem, we employ variational methods for posterior computation, naturally encoding the complexity and multi-modality of the decision making problem. We present a Stein variational gradient descent method to estimate the posterior directly over control parameters, given a cost function and observed state trajectories. We show that this framework leads to successful planning in challenging, non-convex optimal control problems.

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